Climate Impact Prediction System Using Modular Engines
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Solution Overview
Problem
Current methods and technologies face challenges in predicting and identifying the impacts of climate change on surface and groundwater systems, as well as on civil infrastructure, due to the complexity and specificity of data required for accurate risk analysis.
Innovation Solution
A system comprising computational engines and data normalization engines that generate precipitation projections and risk analyses for infrastructure within a geographic area over a defined period, using a combination of IPCC data, local phenomena, and global climate data, and employing machine learning and artificial intelligence methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive climate impact prediction systems are developed, then prediction accuracy and reliability improve, but system complexity and data requirements increase
Solution Approach 1:
The system divides the comprehensive climate impact prediction into separate functional modules: a first engine for precipitation predictions and secondary engines for specific risk analyses (flood risk, drought risk, infrastructure impact). This segmentation allows each module to handle specific tasks with specialized data requirements, improving overall reliability while managing complexity through modular architecture.
Solution Approach 2:
The system employs a universal first engine that generates precipitation predictions applicable to multiple geographic areas and time periods, which then feeds into various secondary engines for different risk analyses. This multi-functional approach allows a single core component to serve multiple purposes, reducing redundant complexity while maintaining high prediction accuracy across diverse climate scenarios.
2Adaptability or versatility
If localized and temporally specific predictions are generated, then prediction relevance and utility improve, but computational time and data processing requirements increase
Solution Approach 1:
The system performs preliminary precipitation predictions using the first engine before running the secondary risk analysis engines. By pre-computing the precipitation data and storing it, the system avoids redundant calculations when running specific risk analyses, thereby reducing overall computational time while maintaining localized and temporal specificity through the structured data organization.
Solution Approach 2:
The system generates precipitation predictions specifically tailored to local geographic areas and temporal periods rather than using generic global data. This local quality approach ensures the highest prediction relevance for specific locations and time frames, and the modular architecture manages the associated computational burden by processing only the necessary local data rather than entire datasets.
3Measurement precision
If multiple data sources and normalization engines are integrated, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The system introduces data normalization engines as intermediary components that bridge multiple data sources with varying formats and quality standards. These normalization engines standardize the data from IPCC, local phenomena, and global climate sources into a consistent format before the predictions are generated, thereby improving measurement precision while managing data processing complexity through centralized normalization logic.
Solution Approach 2:
The system transforms data from multiple sources by changing parameters such as units, resolutions, and formats through the normalization engines. By systematically adjusting data parameters to standard forms, the system achieves high accuracy across diverse data sources without proportionally increasing processing complexity, as the parameter transformations follow standardized protocols.
Data Source
AI summary
Systems, methods, and computer-readable storage media for allowing users to identify impacts of climate change in a given environment. A system receives, from a user, a request to predict climate change impacts for at least one piece of infrastructure within a geographic area over a defined period of time, then executes, in response to the request, a first engine, with the first engine generating precipitation predictions over the defined period of time within the geographic area. The system then executes, in response to the request, at least one secondary engine using the precipitation predictions, where the at least one secondary engine generates a risk analysis due to climate change for the at least one piece of infrastructure within the geographic area.